The present invention relates to computed tomography (CT) imaging systems, and more particularly, improved filtering techniques for enhancing detail and minimizing noise in images generated using CT or other imaging systems.
In a CT system, an x-ray source projects a fan-shaped beam which is collimated to lie within an X-Y plane of a Cartesian coordinate system, termed the “imaging plane.” The x-ray beam passes through the object being imaged, such as a medical patient, and impinges upon an array of radiation detectors. The intensity of the transmitted radiation is dependent upon the attenuation of the x-ray beam by the object and each detector produces a separate electrical signal that is a measurement of the beam attenuation. The attenuation measurements from all the detectors are acquired separately to produce the transmission profile at a particular view angle. Such measurements are often referred to as a “projection” or “projection view” and the data is said to be acquired in “Radon” space.
The source and detector array in a conventional CT system are rotated on a gantry within the imaging plane and around the object so that the angle at which the x-ray beam intersects the object constantly changes during a scan. The resulting set of acquired projection views is a “sinogram” data set that is processed to construct an image that corresponds to a slice taken through the object. The prevailing method for reconstructing an image from 2D data is referred to in the art as the filtered backprojection technique. This process converts the Radon space attenuation measurements in the sinogram into a “real” space image comprised of integers called “CT numbers” or “Hounsfield units”, which are used to control the brightness of a corresponding pixel on a display.
As a result of the CT scanning process, the subject is exposed to a certain degree, or dose, of radiation. The potential cancer or other disease risks associated with the radiation exposure of CT scans has recently become the subject of increasing concerns. To minimize these risks, it is important to reduce the radiation dose level used in CT examinations. Unfortunately, any such reduction in the dose of radiation used in a CT scan leads to an increased level of noise in the measured projection data and the subsequent reconstructed images. Accordingly, in conventional systems, a substantial decrease in the amount of radiation used to perform a CT scan may result in a severe degradation in the diagnostic value of the CT examination.
Techniques for controlling noise in CT, may be employed on raw projection measurements, on log-transformed sinograms, during image reconstruction, or on images after reconstruction have been proposed as indicated below by references 1 through 7. In a conventional shift-invariant filtration method applied during the image reconstruction, the suppression of the high-frequency component in the sinogram is performed with a simple assumption that all the measurements are equally reliable, which may result in severe degradation of spatial resolution. More sophisticated methods have been developed to adaptively smooth the data by taking into account the local statistics in the measurements. Some of these methods are currently implemented on clinical scanners mainly to suppress the streaking artifacts caused by x-ray photon starvation. Many other approaches have also been proposed to incorporate more explicit statistical models and to iteratively restore the log-transformed data by optimizing a penalized weighted least-square or likelihood objective function. These iterative teachings are time consuming and have not been practical to implement in commercial CT systems.
Bilateral filtering is commonly used in photography applications to reduce noise. It is also employed in computer graphics applications to reduce noise in digital images. The bilateral filter is an improvement to the more common Gaussian filter that blurs images by averaging the pixel values in a region surrounding each image pixel to be filtered. Whereas the Gaussian filter weights the surrounding pixel values as a function of their distance from the central pixel to be filtered, the bilateral filter also weights the surrounding pixel values as a function of difference in magnitude of the central pixel and surrounding pixel. The result of this more complex weighting is that sharp edges in the image are preserved.